This proposal requests support for an intensive ten-day course on Computational Image Analysis for Cellular and Developmental Biology. The course is designed for graduate students and postdoctoral fellows, and takes place at the Marine Biological Laboratory in Woods Hole, MA. The course is the first of its kind, giving students formal training in computer vision for the specific analysis of cell and developmental biology image data. Building strong foundations in this topic is critical for pushing cell and developmental biology forward, as imaging has become more and more an indispensable tool in these fields. The course covers the fundamentals of computer vision, taking the students through the sequence of low-, intermediate-, and high-level computer vision tasks that are required to solve image analysis problems in quantitative cellular and developmental biology. The curriculum starts with filtering, thresholding and edge/line/generic feature detection, followed by more sophisticated detection algorithms that employ model fitting. After this introductory block to low-level computer vision tasks, the course moves on to intermediate and higher-level tasks, including object association in space and time (such as tracking) and machine learning tools for phenotype classification. Each topic is covered first by a lecture, generally taught by one of the four core faculty, followed by a 3-4 hour computer programming session where students immediately implement the concepts they learn. There are usually two lectures + computer labs per day. Most programming exercises are individual, giving each student the opportunity to get their hands dirty, while two are team projects allowing the students to also learn and practice methods of code sharing. Over the course's ten days there are three guest lectures by leading researchers in the fields of biological imaging and computer vision, each followed by in-depth discussion, as well as research talks given by the students, faculty and teaching assistants. With this, the core lectures and labs teach the students the fundamentals of computer vision in a logical, continuous manner, the guest lecturers introduce the students to exciting new challenges in imaging and image analysis, and the student/faculty research talks encourage communication between all course participants and give especially the students the opportunity to reflect on how the course can help them with their research.